Novel protein biomarkers for pneumonia and acute exacerbations in COPD: a pilot study.

Introduction: Community-acquired pneumonia (CAP) and acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in high morbidity, mortality, and socio-economic burden. The usage of easily accessible biomarkers informing on disease entity, severity, prognosis, and pathophysiological endotypes is limited in clinical practice. Here, we have analyzed selected plasma markers for their value in differential diagnosis and severity grading in a clinical cohort. Methods: A pilot cohort of hospitalized patients suffering from CAP (n = 27), AECOPD (n = 10), and healthy subjects (n = 22) were characterized clinically. Clinical scores (PSI, CURB, CRB65, GOLD I-IV, and GOLD ABCD) were obtained, and interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-2-receptor (IL-2R), lipopolysaccharide-binding protein (LBP), resistin, thrombospondin-1 (TSP-1), lactotransferrin (LTF), neutrophil gelatinase-associated lipocalin (NGAL), neutrophil-elastase-2 (ELA2), hepatocyte growth factor (HGF), soluble Fas (sFas), as well as TNF-related apoptosis-inducing ligand (TRAIL) were measured in plasma. Results: In CAP patients and healthy volunteers, we found significantly different levels of ELA2, HGF, IL-2R, IL-6, IL-8, LBP, resistin, LTF, and TRAIL. The panel of LBP, sFas, and TRAIL could discriminate between uncomplicated and severe CAP. AECOPD patients showed significantly different levels of LTF and TRAIL compared to healthy subjects. Ensemble feature selection revealed that CAP and AECOPD can be discriminated by IL-6, resistin, together with IL-2R. These factors even allow the differentiation between COPD patients suffering from an exacerbation or pneumonia. Discussion: Taken together, we identified immune mediators in patient plasma that provide information on differential diagnosis and disease severity and can therefore serve as biomarkers. Further studies are required for validation in bigger cohorts.

Surface proteome of plasma extracellular vesicles as mechanistic and clinical biomarkers for malaria.

PURPOSE: Malaria is a life-threatening mosquito-borne disease caused by Plasmodium parasites, mainly in tropical and subtropical countries. Plasmodium falciparum (P. falciparum) is the most prevalent cause on the African continent and responsible for most malaria-related deaths globally. Important medical needs are biomarkers for disease severity or disease outcome. A potential source of easily accessible biomarkers are blood-borne small extracellular vesicles (sEVs). METHODS: We performed an EV Array to find proteins on plasma sEVs that are differentially expressed in malaria patients. Plasma samples from 21 healthy subjects and 15 malaria patients were analyzed. The EV array contained 40 antibodies to capture sEVs, which were then visualized with a cocktail of biotin-conjugated CD9, CD63, and CD81 antibodies. RESULTS: We detected significant differences in the protein decoration of sEVs between healthy subjects and malaria patients. We found CD106 to be the best discrimination marker based on receiver operating characteristic (ROC) analysis with an area under the curve of > 0.974. Additional ensemble feature selection revealed CD106, Osteopontin, CD81, major histocompatibility complex class II DR (HLA-DR), and heparin binding EGF like growth factor (HBEGF) together with thrombocytes to be a feature panel for discrimination between healthy and malaria. TNF-R-II correlated with HLA-A/B/C as well as CD9 with CD81, whereas Osteopontin negatively correlated with CD81 and CD9. Pathway analysis linked the herein identified proteins to IFN-γ signaling. CONCLUSION: sEV-associated proteins can discriminate between healthy individuals and malaria patients and are candidates for future predictive biomarkers. TRIAL REGISTRATION: The trial was registered in the Deutsches Register Klinischer Studien (DRKS-ID: DRKS00012518).

Transcriptional analysis identifies potential biomarkers and molecular regulators in acute malaria infection.

AIMS: Malaria is a serious health threat in tropical countries. The causative parasite of Malaria tropica, the severe form, is the protozoan Plasmodium falciparum. In humans, it infects red blood cells, compromising blood flow and tissue perfusion. This study aims to identify potential biomarkers and RNA networks in leukocyte transcriptomes from patients suffering from Malaria tropica. MATERIALS AND METHODS: We identified differentially regulated mRNAs and microRNAs in peripheral blood leukocytes of healthy donors and Malaria patients. Genes whose expression changes were not attributable to changes in leukocyte composition were used for bioinformatics analysis and network construction. Using a previously published cohort of community-acquired pneumonia (CAP) patients, we established discriminating transcriptomic features versus Malaria. We aimed to establish differences between the patient groups by principal component (PCA) and receiving operator characteristic (ROC) analyses and in silico cell type deconvolution. KEY FINDINGS: We found 870 genes that were significantly differentially expressed between healthy donors and Malaria patients. E2F1, BIRC5 and CCNB1 were identified to be primarily responsible for PCA separation of these two groups. We searched for biological function and found that cell cycle processes were strongly activated. By in silico cell type deconvolution, we attribute this to an expansion of γδ T cells. Additional discrimination between CAP and Malaria yielded 445 differentially expressed genes, among which immune proteasome transcripts PSMB8, PSMB9 and PSMB10 were significantly induced in Malaria. SIGNIFICANCE: We identified transcripts from patient leukocytes that differentiate between healthy, Malaria and CAP, and indicate a biological context with potential pathophysiological relevance.

Transcriptional analysis identifies potential biomarkers and molecular regulators in pneumonia and COPD exacerbation.

Lower respiratory infections, such as community-acquired pneumonia (CAP), and chronic obstructive pulmonary disease (COPD) rank among the most frequent causes of death worldwide. Improved diagnostics and profound pathophysiological insights are urgent clinical needs. In our cohort, we analysed transcriptional networks of peripheral blood mononuclear cells (PBMCs) to identify central regulators and potential biomarkers. We investigated the mRNA- and miRNA-transcriptome of PBMCs of healthy subjects and patients suffering from CAP or AECOPD by microarray and Taqman Low Density Array. Genes that correlated with PBMC composition were eliminated, and remaining differentially expressed genes were grouped into modules. One selected module (120 genes) was particularly suitable to discriminate AECOPD and CAP and most notably contained a subset of five biologically relevant mRNAs that differentiated between CAP and AECOPD with an AUC of 86.1%. Likewise, we identified several microRNAs, e.g. miR-545-3p and miR-519c-3p, which separated AECOPD and CAP. We furthermore retrieved an integrated network of differentially regulated mRNAs and microRNAs and identified HNF4A, MCC and MUC1 as central network regulators or most important discriminatory markers. In summary, transcriptional analysis retrieved potential biomarkers and central molecular features of CAP and AECOPD.

Surface Proteome of Plasma Extracellular Vesicles as Biomarkers for Pneumonia and Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

BACKGROUND: Community-acquired pneumonia (CAP) and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) represent a major burden of disease and death and their differential diagnosis is critical. A potential source of relevant accessible biomarkers are blood-borne small extracellular vesicles (sEVs). METHODS: We performed an extracellular vesicle array to find proteins on plasma sEVs that are differentially expressed and possibly allow the differential diagnosis between CAP and AECOPD. Plasma samples were analyzed from 21 healthy controls, 24 patients with CAP, and 10 with AECOPD . The array contained 40 antibodies to capture sEVs, which were then visualized with a cocktail of biotin-conjugated CD9, CD63, and CD81 antibodies. RESULTS: We detected significant differences in the protein decoration of sEVs between healthy controls and patients with CAP or AECOPD. We found CD45 and CD28 to be the best discrimination markers between CAP and AECOPD in receiver operating characteristic analyses, with an area under the curve >0.92. Additional ensemble feature selection revealed the possibility to distinguish between CAP and AECOPD even if the patient with CAP had COPD, with a panel of CD45, CD28, CTLA4 (cytotoxic T-lymphocyte-associated protein 4), tumor necrosis factor-R-II, and CD16. CONCLUSION: The discrimination of sEV-associated proteins is a minimally invasive method with potential to discriminate between CAP and AECOPD.

An Interactive Macrophage Signal Transduction Map Facilitates Comparative Analyses of High-Throughput Data.

Macrophages (Mϕs) are key players in the coordination of the lifesaving or detrimental immune response against infections. The mechanistic understanding of the functional modulation of Mϕs by pathogens and pharmaceutical interventions at the signal transduction level is still far from complete. The complexity of pathways and their cross-talk benefits from holistic computational approaches. In the present study, we reconstructed a comprehensive, validated, and annotated map of signal transduction pathways in inflammatory Mϕs based on the current literature. In a second step, we selectively expanded this curated map with database knowledge. We provide both versions to the scientific community via a Web platform that is designed to facilitate exploration and analysis of high-throughput data. The platform comes preloaded with logarithmic fold changes from 44 data sets on Mϕ stimulation. We exploited three of these data sets-human primary Mϕs infected with the common lung pathogens Streptococcus pneumoniae, Legionella pneumophila, or Mycobacterium tuberculosis-in a case study to show how our map can be customized with expression data to pinpoint regulated subnetworks and druggable molecules. From the three infection scenarios, we extracted a regulatory core of 41 factors, including TNF, CCL5, CXCL10, IL-18, and IL-12 p40, and identified 140 drugs targeting 16 of them. Our approach promotes a comprehensive systems biology strategy for the exploitation of high-throughput data in the context of Mϕ signal transduction. In conclusion, we provide a set of tools to help scientists unravel details of Mϕ signaling. The interactive version of our Mϕ signal transduction map is accessible online at https://vcells.net/macrophage.

MicroRNAs Constitute a Negative Feedback Loop in Streptococcus pneumoniae-Induced Macrophage Activation.

Streptococcus pneumoniae causes high mortality as a major pneumonia-inducing pathogen. In pneumonia, control of innate immunity is necessary to prevent organ damage. We assessed the role of microRNAs (miRNAs) as regulators in pneumococcal infection of human macrophages. Exposure of primary blood-derived human macrophages with pneumococci resulted in transcriptional changes in several gene clusters and a significant deregulation of 10 microRNAs. Computational network analysis retrieved miRNA-146a as one putatively important regulator of pneumococci-induced host cell activation. Its induction depended on bacterial structural integrity and was completely inhibited by blocking Toll-like receptor 2 (TLR-2) or depleting its mediator MyD88. Furthermore, induction of miRNA-146a release did not require the autocrine feedback of interleukin 1ß and tumor necrosis factor α released from infected macrophages, and it repressed the TLR-2 downstream mediators IRAK-1 and TRAF-6, as well as the inflammatory factors cyclooxygenase 2 and interleukin 1ß. In summary, pneumococci recognition induces a negative feedback loop, preventing excessive inflammation via miR-146a and potentially other miRNAs.